Pub Date : 2019-09-02DOI: 10.1007/978-3-030-55347-0_24
M. Koleva, Y. Poveschenko, L. Vulkov
{"title":"Numerical Simulation of Thermoelastic Nonlinear Waves in Fluid Saturated Porous Media with Non-local Darcy Law","authors":"M. Koleva, Y. Poveschenko, L. Vulkov","doi":"10.1007/978-3-030-55347-0_24","DOIUrl":"https://doi.org/10.1007/978-3-030-55347-0_24","url":null,"abstract":"","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"1 1","pages":"279-289"},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83165430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1007/978-3-030-55347-0_12
A. Wawrzynczak, M. Berendt-Marchel
{"title":"Can the Artificial Neural Network Be Applied to Estimate the Atmospheric Contaminant Transport?","authors":"A. Wawrzynczak, M. Berendt-Marchel","doi":"10.1007/978-3-030-55347-0_12","DOIUrl":"https://doi.org/10.1007/978-3-030-55347-0_12","url":null,"abstract":"","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"1 1","pages":"132-142"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89840575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1007/978-3-030-55347-0_35
H. Chervenkov, V. Spiridonov
{"title":"Sensitivity of Selected ETCCDI Climate Indices from the Calculation Method for Projected Future Climate","authors":"H. Chervenkov, V. Spiridonov","doi":"10.1007/978-3-030-55347-0_35","DOIUrl":"https://doi.org/10.1007/978-3-030-55347-0_35","url":null,"abstract":"","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"48 1","pages":"413-427"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85587747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1007/978-3-030-55347-0_34
H. Chervenkov, K. Slavov
{"title":"ETCCDI Climate Indices for Assessment of the Recent Climate over Southeast Europe","authors":"H. Chervenkov, K. Slavov","doi":"10.1007/978-3-030-55347-0_34","DOIUrl":"https://doi.org/10.1007/978-3-030-55347-0_34","url":null,"abstract":"","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"1 1","pages":"398-412"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88202402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Keynote: A Practical Look at Accelerating Anisotropic Reverse Time Migration","authors":"N. Dai, John Cheng, Wei Wu","doi":"10.1190/HPC2016-006","DOIUrl":"https://doi.org/10.1190/HPC2016-006","url":null,"abstract":"","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74011200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-07-01DOI: 10.4018/IJGHPC.2016070102
H. Qian, Wang Yong, Liuyang Jia, Cai Mengfei
How to manage cloud services efficiently is difficult for large scale of services with frequently changing Quality of Service QoS in cloud computing environment. A multiple-dimension publish/subscribe pub/sub and JXTA based cloud service management mechanism, consists of registry overlay, service publisher and subscriber, is proposed to manage cloud services with active QoS refreshing and fast subscribe capability. The registry overlay with multiple managers cooperating on JXTA, can manage large scale services discovery. The service model with QoS describes a formal model for pub/sub based service management, and a fast subscribing algorithm with filter matrix and multi-dimension index is proposed. The filter matrix helps to reduce candidate services and the multi-dimension index is used to find satisfied services fast. Based on pub/sub and JXTA, the cloud management system is realized. The experiments show that the proposed cloud service management mechanism has good publication and subscribing performance, and is faster than traditional methods for large scale cloud services.
{"title":"Publish/Subscribe and JXTA based Cloud Service Management with QoS","authors":"H. Qian, Wang Yong, Liuyang Jia, Cai Mengfei","doi":"10.4018/IJGHPC.2016070102","DOIUrl":"https://doi.org/10.4018/IJGHPC.2016070102","url":null,"abstract":"How to manage cloud services efficiently is difficult for large scale of services with frequently changing Quality of Service QoS in cloud computing environment. A multiple-dimension publish/subscribe pub/sub and JXTA based cloud service management mechanism, consists of registry overlay, service publisher and subscriber, is proposed to manage cloud services with active QoS refreshing and fast subscribe capability. The registry overlay with multiple managers cooperating on JXTA, can manage large scale services discovery. The service model with QoS describes a formal model for pub/sub based service management, and a fast subscribing algorithm with filter matrix and multi-dimension index is proposed. The filter matrix helps to reduce candidate services and the multi-dimension index is used to find satisfied services fast. Based on pub/sub and JXTA, the cloud management system is realized. The experiments show that the proposed cloud service management mechanism has good publication and subscribing performance, and is faster than traditional methods for large scale cloud services.","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"33 1","pages":"24-37"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80352772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biological sequence comparison is a very common task in Bioinformatics applications. Many parallel solutions have been proposed for this problem, using different HPC platforms, programmed usually with platform-specific languages and frameworks. With this approach, it is difficult to port solutions among different platforms such as CPUs and GPUs, for instance. To tackle this problem, this paper proposes and evaluates an OpenCL parallel solution for Biological Sequence Comparison, which was integrated to the CUDAlign Megabase Sequence Comparison tool. The evaluation of our solution shows we were able to obtain a program for CPUs and GPUs (NVidia and AMD) with basically the same OpenCL code. In addition, in the comparison with SW# and CUDAlign optimized CUDA codes, we show that the performance of our OpenCL version has comparable and, many times, superior performance.
{"title":"Parallel Megabase DNA Sequence Comparison with OpenCL","authors":"Marco Figueiredo, E. Sandes, A. Melo","doi":"10.1109/HiPC.2015.13","DOIUrl":"https://doi.org/10.1109/HiPC.2015.13","url":null,"abstract":"Biological sequence comparison is a very common task in Bioinformatics applications. Many parallel solutions have been proposed for this problem, using different HPC platforms, programmed usually with platform-specific languages and frameworks. With this approach, it is difficult to port solutions among different platforms such as CPUs and GPUs, for instance. To tackle this problem, this paper proposes and evaluates an OpenCL parallel solution for Biological Sequence Comparison, which was integrated to the CUDAlign Megabase Sequence Comparison tool. The evaluation of our solution shows we were able to obtain a program for CPUs and GPUs (NVidia and AMD) with basically the same OpenCL code. In addition, in the comparison with SW# and CUDAlign optimized CUDA codes, we show that the performance of our OpenCL version has comparable and, many times, superior performance.","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"114 1","pages":"436-445"},"PeriodicalIF":0.0,"publicationDate":"2015-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77599240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. L. López, D. Quintín, E. J. Martínez, C. S. Álvaro
Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for instance, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. Thus, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization. In that line, this paper describes the construction of a new hyperspectral processing library for RVC–CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This paper presents the development of the required library functions to implement two of the four stages of the hyperspectral imaging processing chain--endmember and abundances estimation. The results obtained show that the library achieves speedups of 30%, approximately, comparing to an existing software of hyperspectral images analysis; concretely, the endmember estimation step reaches an average speedup of 27.6%, which saves almost 8 seconds in the execution time. It also shows the existence of some bottlenecks, as the communication interfaces among the different actors due to the volume of data to transfer. Finally, it is shown that the library considerably simplifies the implementation process. Thus, experimental results show the potential of a RVC–CAL library for analyzing hyperspectral images in real-time, as it provides enough resources to study the system performance.
{"title":"RVC-CAL library for endmember and abundance estimation in hyperspectral image analysis","authors":"R. L. López, D. Quintín, E. J. Martínez, C. S. Álvaro","doi":"10.1117/12.2194888","DOIUrl":"https://doi.org/10.1117/12.2194888","url":null,"abstract":"Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for instance, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. Thus, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization. In that line, this paper describes the construction of a new hyperspectral processing library for RVC–CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This paper presents the development of the required library functions to implement two of the four stages of the hyperspectral imaging processing chain--endmember and abundances estimation. The results obtained show that the library achieves speedups of 30%, approximately, comparing to an existing software of hyperspectral images analysis; concretely, the endmember estimation step reaches an average speedup of 27.6%, which saves almost 8 seconds in the execution time. It also shows the existence of some bottlenecks, as the communication interfaces among the different actors due to the volume of data to transfer. Finally, it is shown that the library considerably simplifies the implementation process. Thus, experimental results show the potential of a RVC–CAL library for analyzing hyperspectral images in real-time, as it provides enough resources to study the system performance.","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"32 1","pages":"964609"},"PeriodicalIF":0.0,"publicationDate":"2015-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91396792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-07-12DOI: 10.1007/978-3-319-20119-1_18
Michael Kühn
{"title":"Dynamically Adaptable I/O Semantics for High Performance Computing","authors":"Michael Kühn","doi":"10.1007/978-3-319-20119-1_18","DOIUrl":"https://doi.org/10.1007/978-3-319-20119-1_18","url":null,"abstract":"","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"24 1","pages":"240-256"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78513721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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